Oracle 1Z0-1159-26 Certification Sample Questions and Answers
The Oracle Agentic AI for Oracle Database Professional (1Z0-1159-26) Sample Question Set is designed to help you prepare for the Agentic AI for Oracle AI Database Certified Professional certification exam. To become familiar with the actual Oracle Certification exam environment, we suggest you try our Sample Oracle 1Z0-1159-26 Certification Practice Exam.
This Agentic AI for Oracle AI Database Professional certification sample practice test and sample question set are designed for evaluation purposes only. If you want to test your Oracle 1Z0-1159-26 knowledge to identify your areas of improvement and get familiar with the actual exam format, we suggest you prepare with the Premium Agentic AI for Oracle AI Database Certified Professional Certification Practice Exam. Our team of Oracle Cloud Infrastructure experts has designed Questions-Answers for this premium practice exam by collecting inputs from recently certified candidates. Our premium Oracle 1Z0-1159-26 certification practice exam will boost your confidence as well as your actual Oracle Agentic AI for Oracle Database Professional exam result.
Oracle 1Z0-1159-26 Sample Questions:
01. An agritech company runs exact similarity searches over crop-image embeddings. The embedding model is documented for Euclidean distance, and the vectors are not normalized. The application only needs the ten nearest images in the right order; it never displays or stores the distance values. The team wants a cheaper computation than EUCLIDEAN without changing which images are returned or their order.
Which metric should the team use?
a) EUCLIDEAN_SQUARED, which keeps the order and skips the square root step
b) COSINE, which keeps the order while it ignores the vector magnitude
c) HAMMING, which keeps the order by counting the differing dimensions
d) MANHATTAN, which keeps the order and uses cheaper sums of per-dimension differences
02. An online education platform created this index and then tuned a lesson-search query:
CREATE VECTOR INDEX lesson_hnsw ON lessons(emb) ORGANIZATION INMEMORY NEIGHBOR GRAPH WITH TARGET ACCURACY 95;
SELECT lesson_id FROM lessons ORDER BY VECTOR_DISTANCE(emb, :q, EUCLIDEAN) FETCH APPROX FIRST 10 ROWS ONLY;
The query returns correct results with no error, but it is slow and its plan compares the query vector with every row.
Which change lets the query use the index?
a) Lower the query's target accuracy so the optimizer can choose the index
b) Rebuild the index with DBMS_VECTOR.REBUILD_INDEX to refresh the graph
c) Use COSINE in the query so its metric matches the index's metric
d) Raise VECTOR_MEMORY_SIZE so the HNSW graph has room in the vector pool
03. A healthcare IT vendor stores clinical guideline PDFs in a BLOB column. A developer writes a PL/SQL procedure that should extract plain text from each PDF, then chunk and embed it. The procedure calls DBMS_VECTOR.UTL_TO_TEXT, and compilation fails because the function cannot be found. The Oracle Text CONTEXT component is installed.
What should the developer do?
a) Call DBMS_VECTOR.UTL_TO_SUMMARY on each BLOB, then pass its plain-text summary to the chunking step
b) Call DBMS_VECTOR_CHAIN.UTL_TO_TEXT on each BLOB, then pass its plain-text CLOB to the chunking step
c) Apply VECTOR_EMBEDDING to each BLOB directly, then store one vector per PDF with its guideline
d) Pass each BLOB straight to DBMS_VECTOR.UTL_TO_CHUNKS, letting it pull the PDF text out before chunking
04. An energy retailer exposes a tool named METER_READINGS through its Autonomous AI Database MCP Server. Analysts using an MCP client report that the LLM calls the tool for questions about billing disputes, which the tool cannot answer. The Select AI Agent user wants to rewrite the tool's instruction so the LLM calls it only for meter-reading questions.
How should the tool be changed?
a) Disable and re-enable the MCP Server with the adb$feature free-form tag, which reloads each tool with its latest instruction
b) Move the new guidance into the tool_inputs argument descriptions and leave the tool's instruction attribute unchanged
c) Drop the tool, recreate it with CREATE_TOOL using the new instruction, and have MCP clients reconnect
d) Call CREATE_TOOL again with the same tool name and the new instruction; connected clients pick it up on their next call
05. A logistics company is building a RAG pipeline for shipping-policy documents stored as text in a POLICY_DOCS table. A developer writes this query to split each document into chunks, and it fails:
SELECT d.doc_id, VECTOR_CHUNKS(d.body BY WORDS MAX 100 OVERLAP 10) FROM policy_docs d;
What change makes the query work?
a) Wrap the call in VECTOR_EMBEDDING so the SELECT list returns one vector per document
b) Move the VECTOR_CHUNKS call into the FROM clause as a lateral virtual table and select CHUNK_TEXT
c) Move OVERLAP 10 ahead of MAX 100 inside the call and keep the call in the SELECT list
d) Change BY WORDS to BY CHARACTERS and raise MAX to 400 characters, keeping the call in the SELECT list
06. An online marketplace serves product recommendations from an IVF vector index. One fraud-review workload needs noticeably better recall than the default, while every other workload is satisfied with current results. The DBA will not rebuild the index during the current release.
What should the developer change for the fraud-review queries?
a) Recreate the index with a larger NEIGHBOR PARTITIONS value in its PARAMETERS
b) Add WITH TARGET ACCURACY PARAMETERS (EFSEARCH n) with a larger n to those queries
c) Recreate the index with a larger EFCONSTRUCTION value in its PARAMETERS
d) Add WITH TARGET ACCURACY PARAMETERS (NEIGHBOR PARTITION PROBES n) with a larger n to those queries
07. A patent analytics firm splits each patent into chunks and stores one embedding per chunk with its doc_id. An analyst runs:
SELECT doc_id, chunk_id FROM patent_chunks ORDER BY VECTOR_DISTANCE(emb, :q, COSINE) FETCH EXACT FIRST 3 PARTITIONS BY doc_id, 2 ROWS ONLY;
Which description matches the result set?
a) The three nearest chunks from each of the two nearest patents, in distance order
b) Up to two nearest chunks from each of the three nearest patents, in distance order
c) The six nearest chunks overall, in distance order, from any patent
d) Up to two nearest chunks from each of the three nearest patents, grouped together by patent
08. A healthcare-IT vendor's APEX application starts an agent team with DBMS_CLOUD_AI_AGENT.RUN_TEAM to schedule clinic equipment maintenance. The developer created the agent and task without setting enable_human_tool. A support ticket reports that a run never finished, and the team history view shows its state as WAITING_FOR_HUMAN.
What explains this state, and how should the application continue the run?
a) enable_human_tool defaults to true, so the agent asked the user a question; create a new team so the run can start over cleanly
b) short_term_memory_length reached its default of 30 turns; call CLEAR_TEAM to free that memory and let the run continue
c) enable_human_tool defaults to false, so a tool call failed; call SET_TEAM again to restart the paused run in this session
d) enable_human_tool defaults to true, so the agent asked the user a question; call RUN_TEAM with the user's answer as the prompt
09. An insurance company has configured Select AI RAG on Autonomous AI Database. The DBA created a vector index over policy documents in Object Storage with DBMS_CLOUD_AI.CREATE_VECTOR_INDEX and set the AI profile's vector_index_name attribute to it. A claims analyst runs SELECT AI showsql what does the flood rider exclude and receives a SQL statement instead of an answer drawn from the policy documents.
What should the analyst do to get a document-grounded answer?
a) Run the same prompt with the narrate action instead of showsql
b) Run the same prompt with the showprompt action instead of showsql
c) Run the same prompt with the explainsql action instead of showsql
d) Run the same prompt with the chat action instead of showsql
10. A bank runs Autonomous AI Database Serverless, and its fraud table already has an HNSW vector index on the transaction embedding column for interactive lookups. A new quarterly compliance report must list the 20 nearest transactions to a reference pattern, and the auditors require certainty that the result comes from comparing against every vector rather than from an index.
How should the developer write the report query?
a) Use FETCH FIRST 20 ROWS ONLY with no extra keyword
b) Add a NO_VECTOR_INDEX_SCAN hint to FETCH FIRST 20 ROWS ONLY
c) Use FETCH EXACT FIRST 20 ROWS ONLY after the ORDER BY
d) Use FETCH FIRST 20 ROWS ONLY WITH TARGET ACCURACY 100 after the ORDER BY
Answers:
|
Question: 01 Answer: a |
Question: 02 Answer: c |
Question: 03 Answer: b |
Question: 04 Answer: c |
Question: 05 Answer: b |
|
Question: 06 Answer: d |
Question: 07 Answer: b |
Question: 08 Answer: d |
Question: 09 Answer: a |
Question: 10 Answer: c |
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